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Model predictive control = engineering methods for economists /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Model predictive control/ edited by Aris Daniilidis ... [et al.].
其他題名:	engineering methods for economists /
其他作者:	Daniilidis, Aris.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xii, 224 p. :ill. (some col.), digital ;24 cm.
內容註:	Chapter 1. Multi-horizon MPC and Its Application to theIntegrated Power and Thermal Management ofElectriﬁed Vehicles (Qiuhao Hu) -- Chapter 2. Data/Moment-Driven Approaches for FastPredictive Control of Collective Dynamics (Giacomo Albi) -- Chapter 3. Finite-Dimensional Receding Horizon Control ofLinear Time-Varying Parabolic PDEs: StabilityAnalysis and Model-Order Reduction (Behzad Azmi) -- Chapter 4. Solving Hybrid Model Predictive ControlProblems via a Mixed-Integer Approach (Iman Nodozi) -- Chapter 5. nMPyC - A Python Package for Solving OptimalControl Problems via Model Predictive Control (Jonas Schießl) -- Chapter 6. Controllability of Continuous Networks and aKernel-Based Learning Approximation (Michael Herty) -- Chapter 7. Economic Model Predictive Control as aSolution to Markov Decision Processes (Dirk Reinhardt) -- Chapter 8. Reinforcement Learning with Guarantees (Mario Zanon).
Contained By:	Springer Nature eBook
標題:	Predictive control. -
電子資源:	https://doi.org/10.1007/978-3-031-85256-5
ISBN:	9783031852565

Model predictive control = engineering methods for economists /
Model predictive controlengineering methods for economists /[electronic resource] :edited by Aris Daniilidis ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xii, 224 p. :ill. (some col.), digital ;24 cm. - Dynamic modeling and econometrics in economics and finance,v. 312363-8370 ;. - Dynamic modeling and econometrics in economics and finance ;v. 31..

Chapter 1. Multi-horizon MPC and Its Application to theIntegrated Power and Thermal Management ofElectriﬁed Vehicles (Qiuhao Hu) -- Chapter 2. Data/Moment-Driven Approaches for FastPredictive Control of Collective Dynamics (Giacomo Albi) -- Chapter 3. Finite-Dimensional Receding Horizon Control ofLinear Time-Varying Parabolic PDEs: StabilityAnalysis and Model-Order Reduction (Behzad Azmi) -- Chapter 4. Solving Hybrid Model Predictive ControlProblems via a Mixed-Integer Approach (Iman Nodozi) -- Chapter 5. nMPyC - A Python Package for Solving OptimalControl Problems via Model Predictive Control (Jonas Schießl) -- Chapter 6. Controllability of Continuous Networks and aKernel-Based Learning Approximation (Michael Herty) -- Chapter 7. Economic Model Predictive Control as aSolution to Markov Decision Processes (Dirk Reinhardt) -- Chapter 8. Reinforcement Learning with Guarantees (Mario Zanon).

The book explores the field of model predictive control (MPC). It reports on the latest developments in MPC, current applications, and presents various subfields of MPC. The book features topics such as uncertain and stochastic MPC variants, learning and neural network approaches, easy-to-use numerical implementations as well as multi-agent systems and scheduling and coordination tasks. While MPC is rooted in engineering science, this book illustrates the potential of using MPC theory and methods in non-engineering sciences and applications such as economics, finance, and environmental sciences.

ISBN: 9783031852565

Standard No.: 10.1007/978-3-031-85256-5doiSubjects--Topical Terms:

667592
Predictive control.

LC Class. No.: TJ217.6

Dewey Class. No.: 629.8

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505 0 $a Chapter 1. Multi-horizon MPC and Its Application to theIntegrated Power and Thermal Management ofElectriﬁed Vehicles (Qiuhao Hu) -- Chapter 2. Data/Moment-Driven Approaches for FastPredictive Control of Collective Dynamics (Giacomo Albi) -- Chapter 3. Finite-Dimensional Receding Horizon Control ofLinear Time-Varying Parabolic PDEs: StabilityAnalysis and Model-Order Reduction (Behzad Azmi) -- Chapter 4. Solving Hybrid Model Predictive ControlProblems via a Mixed-Integer Approach (Iman Nodozi) -- Chapter 5. nMPyC - A Python Package for Solving OptimalControl Problems via Model Predictive Control (Jonas Schießl) -- Chapter 6. Controllability of Continuous Networks and aKernel-Based Learning Approximation (Michael Herty) -- Chapter 7. Economic Model Predictive Control as aSolution to Markov Decision Processes (Dirk Reinhardt) -- Chapter 8. Reinforcement Learning with Guarantees (Mario Zanon).
520 $a The book explores the field of model predictive control (MPC). It reports on the latest developments in MPC, current applications, and presents various subfields of MPC. The book features topics such as uncertain and stochastic MPC variants, learning and neural network approaches, easy-to-use numerical implementations as well as multi-agent systems and scheduling and coordination tasks. While MPC is rooted in engineering science, this book illustrates the potential of using MPC theory and methods in non-engineering sciences and applications such as economics, finance, and environmental sciences.
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